{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost feature interactions demo\n",
    "\n",
    "This demo demonstrates how to use two XGBoost improvements using H2O XGBoost integration - **feature interaction contraints** and geting **feature interactions** from the model.\n",
    "\n",
    "**More information:**\n",
    "\n",
    "- H2O XGboost interaction constraints documentation: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/interaction_constraints.html\n",
    "- Native XGBoost interaction contraints tutorial: https://xgboost.readthedocs.io/en/latest/tutorials/feature_interaction_constraint.html\n",
    "- H2O XGboost feature interaction documentation: https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/xgboost.html#xgboost-feature-interactions\n",
    "- Original XGBFI package: https://github.com/Far0n/xgbfi\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Interaction Constraints\n",
    "\n",
    "Feature interaction constraints allow users to decide which variables are allowed to interact and which are not.\n",
    "\n",
    "**Potential benefits include:**\n",
    "\n",
    "- Better predictive performance from focusing on interactions that work – whether through domain specific knowledge or algorithms that rank interactions\n",
    "- Less noise in predictions; better generalization\n",
    "- More control to the user on what the model can fit. For example, the user may want to exclude some interactions even if they perform well due to regulatory constraints\n",
    "\n",
    "(Source: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/interaction_constraints.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
       "<td>2 mins 02 secs</td></tr>\n",
       "<tr><td>H2O_cluster_timezone:</td>\n",
       "<td>Europe/Berlin</td></tr>\n",
       "<tr><td>H2O_data_parsing_timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O_cluster_version:</td>\n",
       "<td>3.33.0.99999</td></tr>\n",
       "<tr><td>H2O_cluster_version_age:</td>\n",
       "<td>4 hours and 8 minutes </td></tr>\n",
       "<tr><td>H2O_cluster_name:</td>\n",
       "<td>mori</td></tr>\n",
       "<tr><td>H2O_cluster_total_nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O_cluster_free_memory:</td>\n",
       "<td>4.849 Gb</td></tr>\n",
       "<tr><td>H2O_cluster_total_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O_cluster_status:</td>\n",
       "<td>locked, healthy</td></tr>\n",
       "<tr><td>H2O_connection_url:</td>\n",
       "<td>http://localhost:54321</td></tr>\n",
       "<tr><td>H2O_connection_proxy:</td>\n",
       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
       "<tr><td>H2O_internal_security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O_API_Extensions:</td>\n",
       "<td>XGBoost, Algos, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python_version:</td>\n",
       "<td>3.7.3 candidate</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  --------------------------------\n",
       "H2O_cluster_uptime:         2 mins 02 secs\n",
       "H2O_cluster_timezone:       Europe/Berlin\n",
       "H2O_data_parsing_timezone:  UTC\n",
       "H2O_cluster_version:        3.33.0.99999\n",
       "H2O_cluster_version_age:    4 hours and 8 minutes\n",
       "H2O_cluster_name:           mori\n",
       "H2O_cluster_total_nodes:    1\n",
       "H2O_cluster_free_memory:    4.849 Gb\n",
       "H2O_cluster_total_cores:    8\n",
       "H2O_cluster_allowed_cores:  8\n",
       "H2O_cluster_status:         locked, healthy\n",
       "H2O_connection_url:         http://localhost:54321\n",
       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
       "H2O_internal_security:      False\n",
       "H2O_API_Extensions:         XGBoost, Algos, Core V3, Core V4\n",
       "Python_version:             3.7.3 candidate\n",
       "--------------------------  --------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start h2o\n",
    "import h2o\n",
    "h2o.init(strict_version_check=False, port=54321)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "xgboost Model Build progress: |███████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "from h2o.estimators.xgboost import *\n",
    "# check if the H2O XGBoostEstimator is available\n",
    "assert H2OXGBoostEstimator.available() is True\n",
    "\n",
    "# import data\n",
    "data = h2o.import_file(path = \"../../smalldata/logreg/prostate.csv\")\n",
    "\n",
    "x = list(range(1, data.ncol-2))\n",
    "y = data.names[len(data.names) - 1]\n",
    "\n",
    "ntree = 5\n",
    "\n",
    "h2o_params = {\n",
    "    'eta': 0.3, \n",
    "    'max_depth': 3,  \n",
    "    'ntrees': ntree,\n",
    "    'tree_method': 'hist'\n",
    "} \n",
    "\n",
    "# define interactions as a list of list of names of colums\n",
    "# the lists defines allowed interaction\n",
    "# the interactions of each column with itself are always allowed\n",
    "# so you cannot specified list with one column e.g. [\"PSA\"]\n",
    "h2o_params[\"interaction_constraints\"] = [[\"CAPSULE\", \"AGE\"], [\"PSA\", \"DPROS\"]]\n",
    "\n",
    "# train h2o XGBoost model\n",
    "h2o_model = H2OXGBoostEstimator(**h2o_params)\n",
    "h2o_model.train(x=x, y=y, training_frame=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree index:0\n",
      "Node ID 0 has left child node with index 1 and right child node with index 2. The split feature is CAPSULE.\n",
      "Leaf ID 1.\n",
      "Leaf ID 2.\n",
      "Tree index:1\n",
      "Node ID 0 has left child node with index 1 and right child node with index 2. The split feature is PSA.\n",
      "Node ID 1 has left child node with index 3 and right child node with index 4. The split feature is PSA.\n",
      "Leaf ID 2.\n",
      "Node ID 3 has left child node with index 5 and right child node with index 6. The split feature is DPROS.\n",
      "Leaf ID 4.\n",
      "Leaf ID 5.\n",
      "Leaf ID 6.\n",
      "Tree index:2\n",
      "Node ID 0 has left child node with index 1 and right child node with index 2. The split feature is CAPSULE.\n",
      "Leaf ID 1.\n",
      "Leaf ID 2.\n",
      "Tree index:3\n",
      "Node ID 0 has left child node with index 1 and right child node with index 2. The split feature is PSA.\n",
      "Node ID 1 has left child node with index 3 and right child node with index 4. The split feature is PSA.\n",
      "Leaf ID 2.\n",
      "Node ID 3 has left child node with index 5 and right child node with index 6. The split feature is DPROS.\n",
      "Node ID 4 has left child node with index 7 and right child node with index 8. The split feature is PSA.\n",
      "Leaf ID 5.\n",
      "Leaf ID 6.\n",
      "Leaf ID 7.\n",
      "Leaf ID 8.\n",
      "Tree index:4\n",
      "Node ID 0 has left child node with index 1 and right child node with index 2. The split feature is PSA.\n",
      "Node ID 1 has left child node with index 3 and right child node with index 4. The split feature is PSA.\n",
      "Node ID 2 has left child node with index 5 and right child node with index 6. The split feature is DPROS.\n",
      "Node ID 3 has left child node with index 7 and right child node with index 8. The split feature is DPROS.\n",
      "Node ID 4 has left child node with index 9 and right child node with index 10. The split feature is PSA.\n",
      "Node ID 5 has left child node with index 11 and right child node with index 12. The split feature is PSA.\n",
      "Leaf ID 6.\n",
      "Leaf ID 7.\n",
      "Leaf ID 8.\n",
      "Leaf ID 9.\n",
      "Leaf ID 10.\n",
      "Leaf ID 11.\n",
      "Leaf ID 12.\n"
     ]
    }
   ],
   "source": [
    "# check the trees have allowed structure\n",
    "# so in each tree can be as split feature only \n",
    "from h2o.tree import H2OTree\n",
    "for i in range(0, ntree):\n",
    "    print(\"Tree index:\"+str(i))\n",
    "    tree = H2OTree(h2o_model, i)\n",
    "    for i in range(0, len(tree)):\n",
    "        if tree.left_children[i] == -1:\n",
    "            print(\"Leaf ID {0}.\".format(tree.node_ids[i]))\n",
    "        else:\n",
    "            print(\"Node ID {0} has left child node with index {1} and right child node with index {2}. The split feature is {3}.\".format(tree.node_ids[i], tree.left_children[i], tree.right_children[i], tree.features[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import xgboost as xgb\n",
    "    import pandas as pd\n",
    "    data = pd.read_csv(\"../../smalldata/logreg/prostate.csv\")\n",
    "\n",
    "    y = data[\"GLEASON\"]\n",
    "    x_names = data.columns.to_list()\n",
    "    x_names.remove(\"GLEASON\")\n",
    "    x_names.remove(\"ID\")\n",
    "    x = data[x_names]\n",
    "\n",
    "\n",
    "    D_train = xgb.DMatrix(x, label=y)\n",
    "\n",
    "    param = {\n",
    "        'eta': 0.3, \n",
    "        'max_depth': 3,  \n",
    "        'interaction_constraints': '[[0,1], [3, 5]]', # same as [[\"CAPSULE\", \"AGE\"], [\"PSA\", \"DPROS\"]]\n",
    "        'tree_method': 'hist'\n",
    "    } \n",
    "\n",
    "    steps = ntree\n",
    "\n",
    "    xgboost_model = xgb.train(param, D_train, steps)\n",
    "    # you can compare the H2O XGBoost and native XGBoost have the same tree structure\n",
    "    xgboost_model.trees_to_dataframe()\n",
    "except ImportError:\n",
    "    print(\"module 'xgboost' is not installed\")\n",
    "    xgboost_model = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3600x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3600x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3600x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 3600x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 3600x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot xgboost trees\n",
    "try:\n",
    "    if xgboost_model is not None:\n",
    "        from xgboost import plot_tree\n",
    "        import matplotlib.pyplot as plt\n",
    "        from matplotlib.pylab import rcParams\n",
    "\n",
    "        rcParams['figure.figsize'] = 50, 80\n",
    "        for i in range(0, ntree):\n",
    "            plot_tree(xgboost_model, num_trees=i)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CAPSULE': 82.17504890000001, 'PSA': 112.98286059899998, 'DPROS': 29.485538920000003}\n"
     ]
    }
   ],
   "source": [
    "# show xgboost model variable importance\n",
    "if xgboost_model is not None:\n",
    "    print(xgboost_model.get_score(importance_type='total_gain'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('PSA', 112.98286437988281, 1.0, 0.5029430572294458),\n",
       " ('CAPSULE', 82.175048828125, 0.727323114696645, 0.3658021108991735),\n",
       " ('DPROS', 29.485538482666016, 0.260973543594422, 0.13125483187138065)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show h2o xgboost model variable importance\n",
    "h2o_model.varimp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBFI-like Feature Interaction\n",
    "\n",
    "In the code below, multiple table output provides comprendious insights into higher order interactions between the features of xgboost trees visualized above. Also additional usefull tables summarizing leaf statistics and split value histograms per each feature are provided. Measures used are either one of:\n",
    "\n",
    "**Gain** implies the relative contribution of the corresponding feature to the model calculated by taking each feature's contribution for each tree in the model. A higher value of this metric when compared to another feature implies it is more important for generating a prediction.\n",
    "\n",
    "**Cover** is a metric to measure the number of observations affected by the split. Counted over the specific feature it measures the relative quantity of observations concerned by a feature.\n",
    "\n",
    "**Frequency (FScore)** is the number of times a feature is used in all generated trees. Please note that it does not take the tree-depth nor tree-index of splits a feature occurs into consideration, neither the amount of possible splits of a feature. Hence, it is often suboptimal measure for importance.\n",
    "\n",
    "\n",
    "or their averaged / weighed / ranked alternatives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Interaction Depth 0: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>interaction</th>\n",
       "      <th>gain</th>\n",
       "      <th>fscore</th>\n",
       "      <th>wfscore</th>\n",
       "      <th>average_wfscore</th>\n",
       "      <th>average_gain</th>\n",
       "      <th>expected_gain</th>\n",
       "      <th>gain_rank</th>\n",
       "      <th>fscore_rank</th>\n",
       "      <th>wfscore_rank</th>\n",
       "      <th>avg_wfscore_rank</th>\n",
       "      <th>avg_gain_rank</th>\n",
       "      <th>expected_gain_rank</th>\n",
       "      <th>average_rank</th>\n",
       "      <th>average_tree_index</th>\n",
       "      <th>average_tree_depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>CAPSULE</td>\n",
       "      <td>82.175049</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.087524</td>\n",
       "      <td>82.175049</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>DPROS</td>\n",
       "      <td>29.485539</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.444737</td>\n",
       "      <td>0.111184</td>\n",
       "      <td>7.371385</td>\n",
       "      <td>1.803364</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>PSA</td>\n",
       "      <td>112.982861</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.313158</td>\n",
       "      <td>0.701462</td>\n",
       "      <td>12.553651</td>\n",
       "      <td>102.858399</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    interaction        gain  fscore   wfscore  average_wfscore  average_gain  \\\n",
       "0       CAPSULE   82.175049     2.0  2.000000         1.000000     41.087524   \n",
       "1         DPROS   29.485539     4.0  0.444737         0.111184      7.371385   \n",
       "2           PSA  112.982861     9.0  6.313158         0.701462     12.553651   \n",
       "\n",
       "   expected_gain  gain_rank  fscore_rank  wfscore_rank  avg_wfscore_rank  \\\n",
       "0      82.175049        2.0          3.0           2.0               1.0   \n",
       "1       1.803364        3.0          2.0           3.0               3.0   \n",
       "2     102.858399        1.0          1.0           1.0               2.0   \n",
       "\n",
       "   avg_gain_rank  expected_gain_rank  average_rank  average_tree_index  \\\n",
       "0            1.0                 2.0      1.833333                 1.0   \n",
       "1            3.0                 3.0      2.833333                 3.0   \n",
       "2            2.0                 1.0      1.333333                 3.0   \n",
       "\n",
       "   average_tree_depth  \n",
       "0                0.00  \n",
       "1                1.75  \n",
       "2                1.00  "
      ]
     },
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      "\n",
      "Interaction Depth 1: \n"
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       "      <th>0</th>\n",
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       "      <td>PSA|PSA</td>\n",
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       "      <td>DPROS|PSA</td>\n",
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       "    interaction        gain  fscore   wfscore  average_wfscore  average_gain  \\\n",
       "0       PSA|PSA  122.915955     5.0  3.221053         0.644211     24.583191   \n",
       "1     DPROS|PSA   71.868668     5.0  0.536842         0.107368     14.373734   \n",
       "\n",
       "   expected_gain  gain_rank  fscore_rank  wfscore_rank  avg_wfscore_rank  \\\n",
       "0      80.484941        1.0          1.0           1.0               1.0   \n",
       "1       7.733964        2.0          2.0           2.0               2.0   \n",
       "\n",
       "   avg_gain_rank  expected_gain_rank  average_rank  average_tree_index  \\\n",
       "0            1.0                 1.0           1.0                 3.0   \n",
       "1            2.0                 2.0           2.0                 3.2   \n",
       "\n",
       "   average_tree_depth  \n",
       "0                 1.4  \n",
       "1                 1.8  "
      ]
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      "\n",
      "Interaction Depth 2: \n"
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       "       interaction        gain  fscore   wfscore  average_wfscore  \\\n",
       "0      PSA|PSA|PSA   48.933441     2.0  1.276316         0.638158   \n",
       "1    DPROS|PSA|PSA  153.497696     4.0  0.126316         0.031579   \n",
       "\n",
       "   average_gain  expected_gain  gain_rank  fscore_rank  wfscore_rank  \\\n",
       "0     24.466721      32.149829        2.0          2.0           1.0   \n",
       "1     38.374424       3.009917        1.0          1.0           2.0   \n",
       "\n",
       "   avg_wfscore_rank  avg_gain_rank  expected_gain_rank  average_rank  \\\n",
       "0               1.0            2.0                 1.0           1.5   \n",
       "1               2.0            1.0                 2.0           1.5   \n",
       "\n",
       "   average_tree_index  average_tree_depth  \n",
       "0                 3.5                 2.0  \n",
       "1                 3.0                 2.0  "
      ]
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     "text": [
      "\n",
      "Leaf Statistics: \n"
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       "      <td>454.0</td>\n",
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       "      <td>232.0</td>\n",
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       "      <td></td>\n",
       "      <td>DPROS|PSA|PSA</td>\n",
       "      <td>-0.727657</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
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       "       interaction  sum_leaf_values_left  sum_leaf_values_right  \\\n",
       "0          PSA|PSA              0.000000               1.147155   \n",
       "1          CAPSULE              2.426656               2.908010   \n",
       "2      PSA|PSA|PSA              0.846153               0.628102   \n",
       "3    DPROS|PSA|PSA             -0.727657               1.757318   \n",
       "4              PSA              0.000000               2.132210   \n",
       "\n",
       "   sum_leaf_covers_left  sum_leaf_covers_right  \n",
       "0                   0.0                  241.0  \n",
       "1                 454.0                  306.0  \n",
       "2                 253.0                  232.0  \n",
       "3                  27.0                   21.0  \n",
       "4                   0.0                  245.0  "
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      "\n",
      "CAPSULE Split Value Histogram: \n"
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       "      <th>6</th>\n",
       "      <td></td>\n",
       "      <td>14.7</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td></td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "     split_value  count\n",
       "0            0.7      1\n",
       "1            0.8      2\n",
       "2            2.7      1\n",
       "3           10.6      1\n",
       "4           10.8      1\n",
       "5           12.3      1\n",
       "6           14.7      1\n",
       "7           25.0      1"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "[, , , , , , ]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate multi-level feature interactions\n",
    "h2o_model.feature_interaction()"
   ]
  }
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